Hybrid Artificial Fish Swarm Optimization Algorithm Based on Mutation Operator and Simulated Annealing

ZHANG Mei-feng; SHAO Cheng; GAN Yong; LI Mei-juan

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Hybrid Artificial Fish Swarm Optimization Algorithm Based on Mutation Operator and Simulated Annealing

更新时间：2025-07-16

- Hybrid Artificial Fish Swarm Optimization Algorithm Based on Mutation Operator and Simulated Annealing
- Acta Electronica Sinica Vol. 34, Issue 8, Pages: 1381-1385(2006)
- 作者机构：
  
  1. 大连理工大学电子与信息工程学院,辽宁,大连,116024
  2. 郑州轻工业学院,河南,郑州,450002
  3. 大连理工大学电子与信息工程学院辽宁大连,116024
  4. 郑州轻工业学院河南郑州,450002
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Published：2006
- 稿件说明：
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ZHANG Mei-feng, SHAO Cheng, GAN Yong, et al. Hybrid Artificial Fish Swarm Optimization Algorithm Based on Mutation Operator and Simulated Annealing[J]. Acta Electronica Sinica, 2006, 34(8): 1381-1385.
DOI：

ZHANG Mei-feng, SHAO Cheng, GAN Yong, et al. Hybrid Artificial Fish Swarm Optimization Algorithm Based on Mutation Operator and Simulated Annealing[J]. Acta Electronica Sinica, 2006, 34(8): 1381-1385. DOI：

摘要

人工鱼群算法(AFSA)是一种新型的群智能随机全局优化技术.本文在分析AFSA存在不足的基础上

提出了基于变异算子与模拟退火混合的人工鱼群优化算法.该算法保持了AFSA算法简单、易实现的特点

克服了人工鱼漫无目的随机游动或在非全局极值点的大量聚集

显著提高了算法的运行效率和求解质量.通过函数和实例测试验证

表明了该算法是可行和有效的.

Abstract

Artificial fish swarm algorithm (AFSA) is a stochastic global optimization technique proposed lately.After analyzing the disadvantages of AFSA

this paper presents a hybrid artificial fish swarm optimization algorithm based on mutation operator and simulated annealing.The method is divided into two phases:the AFSA with mutation operator is used to search for the optimum solution

and simulated annealing is applied to optimize the optimum solution.By adding the mutation operator to AFSA in the evolution process

the ability of AFSA to break away from artificial fish stochastic moving without a definite purpose or heavy getting together round the local optimum solution is greatly improved.The hybrid algorithm is as simple for implement as AFSA

but can greatly improve the ability of seeking the global excellent result and convergence property and accuracy.The feasibility and effectiveness of our approach was verified through testing by function and practical problem.The experimental results show that the proposed algorithm is significantly superior to original AFSA.

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